Summary of Quo Vadis, Anomaly Detection? Llms and Vlms in the Spotlight, by Xi Ding and Lei Wang
Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight
by Xi Ding, Lei Wang
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a review of cutting-edge methods for Video Anomaly Detection (VAD) using Large Language Models (LLMs) and Vision-Language Models (VLMs). The LLM/VLM-based approaches address challenges like interpretability, temporal reasoning, and generalization in dynamic scenarios. Key aspects include enhancing interpretability through semantic insights, capturing temporal relationships to detect anomalies, enabling few-shot and zero-shot detection, and addressing open-world anomalies using semantic understanding and motion features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper reviews the latest methods for detecting unusual events in videos, like accidents or people behaving strangely. It talks about how large language models and vision-language models can help make these events easier to understand by providing explanations. The models are also good at recognizing patterns over time, which helps detect anomalies that happen across multiple frames of a video. This could be useful for applications like surveillance systems. The paper also discusses how the models can learn to detect anomalies even when they haven’t been trained on specific examples before. |
Keywords
» Artificial intelligence » Anomaly detection » Few shot » Generalization » Zero shot
